semantic-segmentation-adas-0001

Use Case and High-Level Description

This is a segmentation network to classify each pixel into 20 classes:

  • road

  • sidewalk

  • building

  • wall

  • fence

  • pole

  • traffic light

  • traffic sign

  • vegetation

  • terrain

  • sky

  • person

  • rider

  • car

  • truck

  • bus

  • train

  • motorcycle

  • bicycle

  • ego-vehicle

Example

_images/semantic-segmentation-adas-0001.png

Specification

Metric

Value

Image size

2048x1024

GFlops

58.572

MParams

6.686

Source framework

Caffe*

Accuracy

The quality metrics calculated on 2000 images:

Label

IOU

mean

0.6907

Road

0.910379

Sidewalk

0.630676

Building

0.860139

Wall

0.424166

Fence

0.592632

Pole

0.559078

Traffic Light

0.654779

Traffic Sign

0.648217

Vegetation

0.882593

Terrain

0.620521

Sky

0.976889

Person

0.711653

Rider

0.612787

Car

0.877892

Truck

0.674829

Bus

0.743752

Train

0.358641

Motorcycle

0.600701

Bicycle

0.622246

Ego-Vehicle

0.852932

  • IOU=TP/(TP+FN+FP), where:

    • TP - number of true positive pixels for given class

    • FN - number of false negative pixels for given class

    • FP - number of false positive pixels for given class

Inputs

The blob with BGR image and the shape 1, 3, 1024, 2048 in the format B, C, H, W, where:

  • B – batch size

  • C – number of channels

  • H – image height

  • W – image width

Outputs

The net output is a blob with the shape 1, 1, 1024, 2048 in the format B, C, H, W. It can be treated as a one-channel feature map, where each pixel is a label of one of the classes.

Use Case and High-Level Description

This is a segmentation network to classify each pixel into 20 classes:

  • road

  • sidewalk

  • building

  • wall

  • fence

  • pole

  • traffic light

  • traffic sign

  • vegetation

  • terrain

  • sky

  • person

  • rider

  • car

  • truck

  • bus

  • train

  • motorcycle

  • bicycle

  • ego-vehicle

Example

_images/semantic-segmentation-adas-0001.png

Specification

Metric

Value

Image size

2048x1024

GFlops

58.572

MParams

6.686

Source framework

Caffe*

Accuracy

The quality metrics calculated on 2000 images:

Label

IOU

mean

0.6907

Road

0.910379

Sidewalk

0.630676

Building

0.860139

Wall

0.424166

Fence

0.592632

Pole

0.559078

Traffic Light

0.654779

Traffic Sign

0.648217

Vegetation

0.882593

Terrain

0.620521

Sky

0.976889

Person

0.711653

Rider

0.612787

Car

0.877892

Truck

0.674829

Bus

0.743752

Train

0.358641

Motorcycle

0.600701

Bicycle

0.622246

Ego-Vehicle

0.852932

  • IOU=TP/(TP+FN+FP), where:

    • TP - number of true positive pixels for given class

    • FN - number of false negative pixels for given class

    • FP - number of false positive pixels for given class

Inputs

The blob with BGR image and the shape 1, 3, 1024, 2048 in the format B, C, H, W, where:

  • B – batch size

  • C – number of channels

  • H – image height

  • W – image width

Outputs

The net output is a blob with the shape 1, 1, 1024, 2048 in the format B, C, H, W. It can be treated as a one-channel feature map, where each pixel is a label of one of the classes.

Legal Information

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